This project covers DCEG whole genome scan research and other activities that are part of the Genes, Environment and Health Initiative (GEI). Research activities currently underway include: 1. A Genome-wide Association in a Population-based Lung Cancer Study: This is a grant from GEI (Gene-Environment Initiative) to conduct a whole genome scan on lung cancer and the smoking phenotype on over 5500 subjects from the Environmental And Genetic Lung Cancer Etiology (EAGLE) study and the lung cancer etiology arm of the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial. 2. Case-Control Genome-wide Association Studies (CCGWASs): Studies into the properties of procedures for case-control genome-wide association studies (CCGWASs) that select the SNPs whose chi-square trend tests are largest (or whose corresponding p-values are smallest). We showed that for rare diseases association tests for SNPs are independent if the SNP genotypes are independent in the source population. This result allowed us to develop analytic and simulation techniques to study CCGWASs. These analyses showed that large samples are needed to have a high detection probability (the chance a true disease SNP appears in the top ranks of chi-square values). This project covers DCEG whole genome scan research and other activities that are part of the Genes, Environment and Health Initiative (GEI). Research activities currently underway include: 1. A Genome-wide Association in a Population-based Lung Cancer Study: This is a grant from GEI (Gene-Environment Initiative) to conduct a whole genome scan on lung cancer and the smoking phenotype on over 5500 subjects from the Environmental And Genetic Lung Cancer Etiology (EAGLE) study and the lung cancer etiology arm of the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial. 2. Case-Control Genome-wide Association Studies (CCGWASs): Studies into the properties of procedures for case-control genome-wide association studies (CCGWASs) that select the SNPs whose chi-square trend tests are largest (or whose corresponding p-values are smallest). We showed that for rare diseases association tests for SNPs are independent if the SNP genotypes are independent in the source population. This result allowed us to develop analytic and simulation techniques to study CCGWASs. These analyses showed that large samples are needed to have a high detection probability (the chance a true disease SNP appears in the top ranks of chi-square values).